hls4ml：一个开源的协同设计工作流程，为科学低功耗机器学习设备提供支持
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
摘要：
适用于节能设备和系统的可扩展机器学习算法、软件和诊断工具在广泛的应用领域中极具价值。在科学领域，实时近传感器处理可以极大地改善实验设计并加速科学发现。为了支持领域科学家，我们开发了hls4ml，这是一个开源的软硬件协同设计工作流，用于解释和转换机器学习算法，以便使用FPGA和ASIC技术实现。我们扩展了以前的hls4ml工作，将功能和技术扩展到低功耗实现和提高可用性：新的Python API，量化感知修剪，端到端FPGA工作流，低功耗的长管道内核，以及新的设备后端，包括ASIC工作流。总之，这些以及hls4ml的持续努力将为新一代领域科学家提供可访问的，高效的和强大的工具，用于机器学习加速发现。
英文摘要：
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. In scientific domains, real-time near-sensor processing can drastically improve experimental design and accelerate scientific discoveries. To support domain scientists, we have developed hls4ml, an open-source software-hardware codesign workflow to interpret and translate machine learning algorithms for implementation with both FPGA and ASIC technologies. We expand on previous hls4ml work by extending capabilities and techniques towards low-power implementations and increased usability: new Python APIs, quantization-aware pruning, end-to-end FPGA workflows, long pipeline kernels for low power, and new device backends include an ASIC workflow. Taken together, these and continued efforts in hls4ml will arm a new generation of domain scientists with accessible, efficient, and powerful tools for machine-learning-accelerated discovery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
